Efficient concept clustering for ontology learning using an event life cycle on the web
Proceedings of the 2008 ACM symposium on Applied computing
Knowledge Discovery and Digital Cartography for the ALS (Linguistic Atlas of Sicily) Project
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Towards a scalable and robust multi-tenancy SaaS
Proceedings of the Second Asia-Pacific Symposium on Internetware
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Schema matching has been historically difficult to automate. Most previous studies have tried to find matches by exploiting information on schema and data instances. However, schema and data instances cannot fully capture the semantic information of the databases. Therefore, some attributes can be matched to improper attributes. To address this problem, we propose a schema matching framework that supports identification of the correct matches by extracting the semantics from ontologies. In ontologies, two concepts share similar semantics in their common parent. In addition, the parent can be further used to quantify a similarity between them. By combining this idea with effective contemporary mapping algorithms, we perform an ontology-driven semantic matching in multiple data sources. Experimental results indicate that the proposed method successfully identifies higher accurate matches than those of previous works.